TensorFlow使用CNN实现中文文本分类

曾经终败给现在 2022-04-17 03:35 524阅读 0赞

# TensorFlow使用CNN实现中文文本分类 #

读研期间使用过TensorFlow实现过简单的CNN情感分析（分类），当然这是比较low的二分类情况，后来进行多分类情况。但之前的学习基本上都是在英文词库上训练的。断断续续，想整理一下手头的项目资料，于是就拾起读研期间的文本分类的小项目，花了一点时间，把原来英文文本分类的项目，应用在中文文本分类，效果还不错，在THUCNews中文数据集上，准确率93.9%左右，老规矩，先上源码地址

Github项目源码：[nlp-learning-tutorials/THUCNews at master · PanJinquan/nlp-learning-tutorials · GitHub][nlp-learning-tutorials_THUCNews at master _ PanJinquan_nlp-learning-tutorials _ GitHub]， **记得给个“Star”哈**

--------------------

**目录**

TensorFlow使用CNN实现中文文本分类

一、项目介绍

1.1 目录结构

1.2 THUCNews数据集

二、CNN模型结构

三、文本预处理

1、jieba中文分词

2、gensim训练word2vec模型

四、训练过程

五、测试过程

--------------------

## 一、项目介绍 ##

### 1.1 目录结构 ###

其他资源地址：

>  *  1.THUCTC官方数据集，链接: [THUCTC: 一个高效的中文文本分类工具][THUCTC_]
>  *  2.THUCTC百度网盘，链接: [百度网盘 请输入提取码][Link 1] 提取码: bbpe
>  *  3.已经训练好的word2vec模型：链接: [百度网盘 请输入提取码][Link 2] 提取码: mtrj
>  *  4.使用词向量处理的THUCNews数据：链接: [百度网盘 请输入提取码][Link 3] 提取码: m9dx

![20181114190800584.png][]

### 1.2 THUCNews数据集 ###

THUCNews是根据新浪新闻RSS订阅频道2005~2011年间的历史数据筛选过滤生成，包含74万篇新闻文档（2.19 GB），均为UTF-8纯文本格式。我们在原始新浪新闻分类体系的基础上，重新整合划分出14个候选分类类别：财经、彩票、房产、股票、家居、教育、科技、社会、时尚、时政、体育、星座、游戏、娱乐。相关介绍，可以看这里[THUCTC: 一个高效的中文文本分类工具][THUCTC_ 1]

> **下载地址：**  
> 1.官方数据集下载链接: http://thuctc.thunlp.org/message  
> 2.百度网盘下载链接: https://pan.baidu.com/s/1DT5xY9m2yfu1YGaGxpWiBQ 提取码: bbpe

## 二、CNN模型结构 ##

CNN文本分类的网络结，如下：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2d1eXVlYWxpYW4_size_16_color_FFFFFF_t_70][]

> 简单分析一下：
> 
> （1）我们假定输入CNN的数据是二维的，其中每一行表示一个样本（即一个字词），如图中“I”、“like”等。每一个样本（字词）有d个维度，可以看成是词向量长度，即每个字词的维度，程序中用embedding\_dim表示。
> 
> （2）使用CNN的卷积对这个二维数据进行卷积：在图像的CNN卷积中，卷积核的大小一般是3\*3，5\*5等，但在NLP中就不就不能这么搞了，因为这里的输入数据每行是一个样本了！假设卷积核的大小为\[filter\_height,filter\_width\],那么卷积核的高度filter\_height可以为1，2，3等任意值，而宽度filter\_width只能是embedding\_dim的大小，这样才能把完整的样本框进去！

下面是使用TensorFlow实现的CNN文本分类网络：TextCNN,

>  max\_sentence\_length = 300 \# 最大句子长度，也就是说文本样本中字词的最大长度，不足补零，多余的截断  
>  embedding\_dim = 128 \#词向量长度，即每个字词的维度  
>  filter\_sizes = \[3, 4, 5, 6\] \#卷积核大小  
>  num\_filters = 200  \# Number of filters per filter size 卷价个数  
>  base\_lr=0.001      \# 学习率  
>  dropout\_keep\_prob = 0.5  
>  l2\_reg\_lambda = 0.0  \# "L2 regularization lambda (default: 0.0)

import tensorflow as tf
    import numpy as np
    
    
    class TextCNN(object):
        '''
        A CNN for text classification
        Uses and embedding layer, followed by a convolutional, max-pooling and softmax layer.
        '''
        def __init__(
            self, sequence_length, num_classes,
            embedding_size, filter_sizes, num_filters, l2_reg_lambda=0.0):
    
            # Placeholders for input, output, dropout
            self.input_x = tf.placeholder(tf.float32, [None, sequence_length, embedding_size], name = "input_x")
            self.input_y = tf.placeholder(tf.float32, [None, num_classes], name = "input_y")
            self.dropout_keep_prob = tf.placeholder(tf.float32, name = "dropout_keep_prob")
    	    
    	    # Keeping track of l2 regularization loss (optional)
            l2_loss = tf.constant(0.0)
    
    	    # Embedding layer
                # self.embedded_chars = [None(batch_size), sequence_size, embedding_size]
                # self.embedded_chars = [None(batch_size), sequence_size, embedding_size, 1(num_channels)]
            self.embedded_chars = self.input_x
            self.embedded_chars_expended = tf.expand_dims(self.embedded_chars, -1)
    
    	    # Create a convolution + maxpool layer for each filter size
            pooled_outputs = []
            for i, filter_size in enumerate(filter_sizes):# "filter_sizes", "3,4,5",
                
                with tf.name_scope("conv-maxpool-%s" % filter_size):
    	            # Convolution layer
                    filter_shape = [filter_size, embedding_size, 1, num_filters] # num_filters= 200
                    # filter_shape =[height, width, in_channels, output_channels]
    
                    W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name="W")
                    b = tf.Variable(tf.constant(0.1, shape=[num_filters]), name="b")
                    conv = tf.nn.conv2d(self.embedded_chars_expended,
    		                            W,
    		                            strides=[1,1,1,1],
    		                            padding="VALID",
    		                            name="conv")
    		        # Apply nonlinearity
                    h = tf.nn.relu(tf.nn.bias_add(conv, b), name = "relu")
    		        # Maxpooling over the outputs
                    pooled = tf.nn.max_pool(
    		        h,
    		        ksize=[1, sequence_length - filter_size + 1, 1, 1],
    		        strides=[1,1,1,1],
    		        padding="VALID",
    		        name="pool")
                    pooled_outputs.append(pooled)
    
    	    # Combine all the pooled features
            num_filters_total = num_filters * len(filter_sizes)
            self.h_pool = tf.concat(pooled_outputs, 3)
            # self.h_pool = tf.concat(3, pooled_outputs)
            self.h_pool_flat = tf.reshape(self.h_pool, [-1, num_filters_total])
    
    	    # Add dropout
            with tf.name_scope("dropout"):
    	        self.h_drop = tf.nn.dropout(self.h_pool_flat, self.dropout_keep_prob)
    	    
    	    # Final (unnomalized) scores and predictions
            with tf.name_scope("output"):
                W = tf.get_variable(
    		                            "W",
    		                            shape = [num_filters_total, num_classes],
                                        initializer = tf.contrib.layers.xavier_initializer())
                
                b = tf.Variable(tf.constant(0.1, shape=[num_classes], name = "b"))
                l2_loss += tf.nn.l2_loss(W)
                l2_loss += tf.nn.l2_loss(b)
                self.scores = tf.nn.xw_plus_b(self.h_drop, W, b, name = "scores")
                self.predictions = tf.argmax(self.scores, 1, name = "predictions")
    
    	    # Calculate Mean cross-entropy loss
            with tf.name_scope("loss"):
                losses = tf.nn.softmax_cross_entropy_with_logits(logits = self.scores, labels = self.input_y)
                self.loss = tf.reduce_mean(losses) + l2_reg_lambda * l2_loss
    	    
    	    # Accuracy
            with tf.name_scope("accuracy"):
                correct_predictions = tf.equal(self.predictions, tf.argmax(self.input_y, 1))
                self.accuracy = tf.reduce_mean(tf.cast(correct_predictions, "float"), name = "accuracy")

--------------------

## 三、文本预处理 ##

本博客使用jieba工具进行中文分词，使用词进行训练会比使用字进行训练，效果更好。

这部分：已经在《[使用gensim训练中文语料word2vec][gensim_word2vec]》[使用gensim训练中文语料word2vec\_pan\_jinquan的博客-CSDN博客\_gensim 中文][gensim_word2vec]，详解讲解，自己看吧！

#  #

### 1、jieba中文分词 ###

这个需要自己安装：`pip install jieba` 或者`pip3 install jieba`

### 2、gensim训练word2vec模型 ###

这里使用jieba工具对THUCNews数据集进行分词，并利用gensim训练基于THUCNews的word2vec模型，这里提供已经训练好的word2vec模型：*链接:* *https://pan.baidu.com/s/1n4ZgiF0gbY0zsK0706wZiw* *提取码:* *mtrj *

### 2、THUCNews数据处理 ###

有了word2vec模型，我就可以用word2vec词向量处理THUCNews数据：先使用jieba工具将中文句子转为字词，再将字词根据word2vec模型转为embadding 的索引，有了索引就可以获得词向量embadding  。这里并把这些索引数据保存为npy文件。后续训练时，CNN网络只需要读取这些npy文件，并将索引转为embadding，就可以进行训练了。

> 处理好的THUCNews数据下载地址：链接:*https://pan.baidu.com/s/12Hdf36QafQ3y6KgV\_vLTsw*提取码: m9dx

下面的代码实现的功能：使用jieba工具将中文句子转为字词，再将字词根据word2vec模型转为embadding 的索引矩阵，然后把这些索引矩阵保存下来（\*.npy文件）,源代码中batchSize=20000表示：将20000中文TXT文件处理成字词，转为索引矩阵并保存为一个\*.npy文件，相当于将20000中文TXT文件保存为一个\*.npy文件，主要是为了压缩数据，避免单个文件过大的情况。

# -*-coding: utf-8 -*-
    """
        @Project: nlp-learning-tutorials
        @File   : create_word2vec.py
        @Author : panjq
        @E-mail : pan_jinquan@163.com
        @Date   : 2018-11-08 17:37:21
    """
    from gensim.models import Word2Vec
    import random
    import numpy as np
    import os
    import math
    from utils import files_processing,segment
    
    
    def info_npy(file_list):
        sizes=0
        for file in file_list:
            data = np.load(file)
            print("data.shape:{}".format(data.shape))
            size = data.shape[0]
            sizes+=size
        print("files nums:{}, data nums:{}".format(len(file_list), sizes))
        return sizes
    
    def save_multi_file(files_list,labels_list,word2vec_path,out_dir,prefix,batchSize,max_sentence_length,labels_set=None,shuffle=False):
        '''
        将文件内容映射为索引矩阵，并且将数据保存为多个文件
        :param files_list:
        :param labels_list:
        :param word2vec_path: word2vec模型的位置
        :param out_dir: 文件保存的目录
        :param prefix:  保存文件的前缀名
        :param batchSize: 将多个文件内容保存为一个文件
        :param labels_set: labels集合
        :return:
        '''
        if not os.path.exists(out_dir):
            os.mkdir(out_dir)
    
        # 把该目录下的所有文件都删除
        files_processing.delete_dir_file(out_dir)
    
        if shuffle:
            random.seed(100)
            random.shuffle(files_list)
            random.seed(100)
            random.shuffle(labels_list)
    
        sample_num = len(files_list)
        w2vModel=load_wordVectors(word2vec_path)
        if labels_set is None:
            labels_set= files_processing.get_labels_set(label_list)
    
        labels_list, labels_set = files_processing.labels_encoding(labels_list, labels_set)
        labels_list=labels_list.tolist()
        batchNum = int(math.ceil(1.0 * sample_num / batchSize))
        for i in range(batchNum):
            start = i * batchSize
            end = min((i + 1) * batchSize, sample_num)
            batch_files = files_list[start:end]
            batch_labels = labels_list[start:end]
    
            # 读取文件内容，字词分割
            batch_content = files_processing.read_files_list_to_segment(batch_files,
                                                                        max_sentence_length,
                                                                        padding_token='<PAD>',
                                                                        segment_type='word')
            # 将字词转为索引矩阵
            batch_indexMat = word2indexMat(w2vModel, batch_content, max_sentence_length)
            batch_labels=np.asarray(batch_labels)
            batch_labels = batch_labels.reshape([len(batch_labels), 1])
    
            # 保存*.npy文件
            filename = os.path.join(out_dir,prefix + '{0}.npy'.format(i))
            labels_indexMat = cat_labels_indexMat(batch_labels, batch_indexMat)
            np.save(filename, labels_indexMat)
            print('step:{}/{}, save:{}, data.shape{}'.format(i,batchNum,filename,labels_indexMat.shape))
    
    
    def cat_labels_indexMat(labels,indexMat):
        indexMat_labels = np.concatenate([labels,indexMat], axis=1)
        return indexMat_labels
    
    def split_labels_indexMat(indexMat_labels,label_index=0):
        labels = indexMat_labels[:, 0:label_index+1]     # 第一列是labels
        indexMat = indexMat_labels[:, label_index+1:]  # 其余是indexMat
        return labels, indexMat
    
    def load_wordVectors(word2vec_path):
        w2vModel = Word2Vec.load(word2vec_path)
        return w2vModel
    
    def word2vector_lookup(w2vModel, sentences):
        '''
        将字词转换为词向量
        :param w2vModel: word2vector模型
        :param sentences: type->list[list[str]]
        :return: sentences对应的词向量,type->list[list[ndarray[list]]
        '''
        all_vectors = []
        embeddingDim = w2vModel.vector_size
        embeddingUnknown = [0 for i in range(embeddingDim)]
        for sentence in sentences:
            this_vector = []
            for word in sentence:
                if word in w2vModel.wv.vocab:
                    v=w2vModel[word]
                    this_vector.append(v)
                else:
                    this_vector.append(embeddingUnknown)
            all_vectors.append(this_vector)
        all_vectors=np.array(all_vectors)
        return all_vectors
    
    def word2indexMat(w2vModel, sentences, max_sentence_length):
        '''
        将字词word转为索引矩阵
        :param w2vModel:
        :param sentences:
        :param max_sentence_length:
        :return:
        '''
        nums_sample=len(sentences)
        indexMat = np.zeros((nums_sample, max_sentence_length), dtype='int32')
        rows = 0
        for sentence in sentences:
            indexCounter = 0
            for word in sentence:
                try:
                    index = w2vModel.wv.vocab[word].index  # 获得单词word的下标
                    indexMat[rows][indexCounter] = index
                except :
                    indexMat[rows][indexCounter] = 0  # Vector for unkown words
                indexCounter = indexCounter + 1
                if indexCounter >= max_sentence_length:
                    break
            rows+=1
        return indexMat
    
    def indexMat2word(w2vModel, indexMat, max_sentence_length=None):
        '''
        将索引矩阵转为字词word
        :param w2vModel:
        :param indexMat:
        :param max_sentence_length:
        :return:
        '''
        if max_sentence_length is None:
            row,col =indexMat.shape
            max_sentence_length=col
        sentences=[]
        for Mat in indexMat:
            indexCounter = 0
            sentence=[]
            for index in Mat:
                try:
                    word = w2vModel.wv.index2word[index] # 获得单词word的下标
                    sentence+=[word]
                except :
                    sentence+=['<PAD>']
                indexCounter = indexCounter + 1
                if indexCounter >= max_sentence_length:
                    break
            sentences.append(sentence)
        return sentences
    
    def save_indexMat(indexMat,path):
        np.save(path, indexMat)
    
    def load_indexMat(path):
        indexMat = np.load(path)
        return indexMat
    
    def indexMat2vector_lookup(w2vModel,indexMat):
        '''
        将索引矩阵转为词向量
        :param w2vModel:
        :param indexMat:
        :return: 词向量
        '''
        all_vectors = w2vModel.wv.vectors[indexMat]
        return all_vectors
    
    def pos_neg_test():
        positive_data_file = "./data/ham_5000.utf8"
        negative_data_file = './data/spam_5000.utf8'
    
        word2vec_path = 'out/trained_word2vec.model'
        sentences, labels = files_processing.load_pos_neg_files(positive_data_file, negative_data_file)
        # embedding_test(positive_data_file,negative_data_file)
        sentences, max_document_length = segment.padding_sentences(sentences, '<PADDING>', padding_sentence_length=190)
        # train_wordVectors(sentences,embedding_size=128,word2vec_path=word2vec_path) # 训练word2vec，并保存word2vec_path
        w2vModel=load_wordVectors(word2vec_path) #加载训练好的word2vec模型
    
        '''
        转换词向量提供有两种方法：
        [1]直接转换：根据字词直接映射到词向量：word2vector_lookup
        [2]间接转换：先将字词转为索引矩阵，再由索引矩阵映射到词向量：word2indexMat->indexMat2vector_lookup
        '''
        # [1]根据字词直接映射到词向量
        x1=word2vector_lookup(w2vModel, sentences)
    
        # [2]先将字词转为索引矩阵，再由索引矩阵映射到词向量
        indexMat_path = 'out/indexMat.npy'
        indexMat=word2indexMat(w2vModel, sentences, max_sentence_length=190) # 将字词转为索引矩阵
        save_indexMat(indexMat, indexMat_path)
        x2=indexMat2vector_lookup(w2vModel, indexMat) # 索引矩阵映射到词向量
        print("x.shape = {}".format(x2.shape))# shape=(10000, 190, 128)->(样本个数10000,每个样本的字词个数190，每个字词的向量长度128)
    
    if __name__=='__main__':
        # THUCNews_path='/home/ubuntu/project/tfTest/THUCNews/test'
        # THUCNews_path='/home/ubuntu/project/tfTest/THUCNews/spam'
        THUCNews_path='/home/ubuntu/project/tfTest/THUCNews/THUCNews'
        # 读取所有文件列表
        files_list, label_list = files_processing.gen_files_labels(THUCNews_path)
    
        max_sentence_length=300
        word2vec_path="../../word2vec/models/THUCNews_word2Vec/THUCNews_word2Vec_128.model"
    
        # 获得标签集合，并保存在本地
        # labels_set=['星座','财经','教育']
        # labels_set = files_processing.get_labels_set(label_list)
        labels_file='../data/THUCNews_labels.txt'
        # files_processing.write_txt(labels_file, labels_set)
    
        # 将数据划分为train val数据集
        train_files, train_label, val_files, val_label= files_processing.split_train_val_list(files_list, label_list, facror=0.9, shuffle=True)
    
        # contents, labels=files_processing.read_files_labels(files_list,label_list)
        # word2vec_path = 'out/trained_word2vec.model'
        train_out_dir='../data/train_data'
        prefix='train_data'
        batchSize=20000
        labels_set=files_processing.read_txt(labels_file)
        # labels_set2 = files_processing.read_txt(labels_file)
        save_multi_file(files_list=train_files,
                        labels_list=train_label,
                        word2vec_path=word2vec_path,
                        out_dir=train_out_dir,
                        prefix=prefix,
                        batchSize=batchSize,
                        max_sentence_length=max_sentence_length,
                        labels_set=labels_set,
                        shuffle=True)
        print("*******************************************************")
        val_out_dir='../data/val_data'
        prefix='val_data'
        save_multi_file(files_list=val_files,
                        labels_list=val_label,
                        word2vec_path=word2vec_path,
                        out_dir=val_out_dir,
                        prefix=prefix,
                        batchSize=batchSize,
                        max_sentence_length=max_sentence_length,
                        labels_set=labels_set,
                        shuffle=True)

##  ##

--------------------

## 四、训练过程 ##

训练代码如下，注意，Github上不能上传大文件，所以你需要把上面提供的文件都下载下来，并放在对应的文件目录，就可以训练了。

训练中需要读取训练数据，即\*.npy文件，\*.npy文件保存的是索引数据，因此需要转为CNN的embadding数据：这个过程由函数：***indexMat2vector\_lookup***完成：***train\_batch\_data = create\_word2vec.indexMat2vector\_lookup(w2vModel, train\_batch\_data)***

#! /usr/bin/env python
    # encoding: utf-8
    
    import tensorflow as tf
    import numpy as np
    import os
    from text_cnn import TextCNN
    from utils import create_batch_data, create_word2vec, files_processing
    
    def train(train_dir,val_dir,labels_file,word2vec_path,batch_size,max_steps,log_step,val_step,snapshot,out_dir):
        '''
        训练...
        :param train_dir: 训练数据目录
        :param val_dir:   val数据目录
        :param labels_file:  labels文件目录
        :param word2vec_path: 词向量模型文件
        :param batch_size: batch size
        :param max_steps:  最大迭代次数
        :param log_step:  log显示间隔
        :param val_step:  测试间隔
        :param snapshot:  保存模型间隔
        :param out_dir:   模型ckpt和summaries输出的目录
        :return:
        '''
    
        max_sentence_length = 300
        embedding_dim = 128
        filter_sizes = [3, 4, 5, 6]
        num_filters = 200  # Number of filters per filter size
        base_lr=0.001# 学习率
        dropout_keep_prob = 0.5
        l2_reg_lambda = 0.0  # "L2 regularization lambda (default: 0.0)
    
    
        allow_soft_placement = True  # 如果你指定的设备不存在，允许TF自动分配设备
        log_device_placement = False  # 是否打印设备分配日志
    
    
        print("Loading data...")
        w2vModel = create_word2vec.load_wordVectors(word2vec_path)
    
        labels_set = files_processing.read_txt(labels_file)
        labels_nums = len(labels_set)
    
        train_file_list = create_batch_data.get_file_list(file_dir=train_dir, postfix='*.npy')
        train_batch = create_batch_data.get_data_batch(train_file_list, labels_nums=labels_nums, batch_size=batch_size,
                                                       shuffle=False, one_hot=True)
    
        val_file_list = create_batch_data.get_file_list(file_dir=val_dir, postfix='*.npy')
        val_batch = create_batch_data.get_data_batch(val_file_list, labels_nums=labels_nums, batch_size=batch_size,
                                                     shuffle=False, one_hot=True)
    
        print("train data info *****************************")
        train_nums=create_word2vec.info_npy(train_file_list)
        print("val data   info *****************************")
        val_nums = create_word2vec.info_npy(val_file_list)
        print("labels_set info *****************************")
        files_processing.info_labels_set(labels_set)
    
        # Training
        with tf.Graph().as_default():
            session_conf = tf.ConfigProto(allow_soft_placement = allow_soft_placement,log_device_placement = log_device_placement)
            sess = tf.Session(config = session_conf)
            with sess.as_default():
                cnn = TextCNN(sequence_length = max_sentence_length,
                              num_classes = labels_nums,
                              embedding_size = embedding_dim,
                              filter_sizes = filter_sizes,
                              num_filters = num_filters,
                              l2_reg_lambda = l2_reg_lambda)
    
                # Define Training procedure
                global_step = tf.Variable(0, name="global_step", trainable=False)
                optimizer = tf.train.AdamOptimizer(learning_rate=base_lr)
                # optimizer = tf.train.MomentumOptimizer(learning_rate=0.01, momentum=0.9)
                grads_and_vars = optimizer.compute_gradients(cnn.loss)
                train_op = optimizer.apply_gradients(grads_and_vars, global_step=global_step)
    
                # Keep track of gradient values and sparsity (optional)
                grad_summaries = []
                for g, v in grads_and_vars:
                    if g is not None:
                        grad_hist_summary = tf.summary.histogram("{}/grad/hist".format(v.name), g)
                        sparsity_summary = tf.summary.scalar("{}/grad/sparsity".format(v.name), tf.nn.zero_fraction(g))
                        grad_summaries.append(grad_hist_summary)
                        grad_summaries.append(sparsity_summary)
                grad_summaries_merged = tf.summary.merge(grad_summaries)
    
                # Output directory for models and summaries
                print("Writing to {}\n".format(out_dir))
    
                # Summaries for loss and accuracy
                loss_summary = tf.summary.scalar("loss", cnn.loss)
                acc_summary = tf.summary.scalar("accuracy", cnn.accuracy)
    
                # Train Summaries
                train_summary_op = tf.summary.merge([loss_summary, acc_summary, grad_summaries_merged])
                train_summary_dir = os.path.join(out_dir, "summaries", "train")
                train_summary_writer = tf.summary.FileWriter(train_summary_dir, sess.graph)
    
                # Dev summaries
                dev_summary_op = tf.summary.merge([loss_summary, acc_summary])
                dev_summary_dir = os.path.join(out_dir, "summaries", "dev")
                dev_summary_writer = tf.summary.FileWriter(dev_summary_dir, sess.graph)
    
                # Checkpoint directory. Tensorflow assumes this directory already exists so we need to create it
                checkpoint_dir = os.path.abspath(os.path.join(out_dir, "checkpoints"))
                checkpoint_prefix = os.path.join(checkpoint_dir, "model")
                if not os.path.exists(checkpoint_dir):
                    os.makedirs(checkpoint_dir)
                saver = tf.train.Saver(tf.global_variables(), max_to_keep=5)
    
                # Initialize all variables
                sess.run(tf.global_variables_initializer())
    
                def train_step(x_batch, y_batch):
                    """
                    A single training step
                    """
                    feed_dict = {
                      cnn.input_x: x_batch,
                      cnn.input_y: y_batch,
                      cnn.dropout_keep_prob: dropout_keep_prob
                    }
                    _, step, summaries, loss, accuracy = sess.run(
                        [train_op, global_step, train_summary_op, cnn.loss, cnn.accuracy],
                        feed_dict)
                    if step % log_step==0:
                        print("training: step {}, loss {:g}, acc {:g}".format(step, loss, accuracy))
                    train_summary_writer.add_summary(summaries, step)
    
                def dev_step(x_batch, y_batch, writer=None):
                    """
                    Evaluates model on a dev set
                    """
                    feed_dict = {
                      cnn.input_x: x_batch,
                      cnn.input_y: y_batch,
                      cnn.dropout_keep_prob: 1.0
                    }
                    step, summaries, loss, accuracy = sess.run(
                        [global_step, dev_summary_op, cnn.loss, cnn.accuracy],
                        feed_dict)
                    if writer:
                        writer.add_summary(summaries, step)
                    return loss, accuracy
    
                for i in range(max_steps):
                    train_batch_data, train_batch_label = create_batch_data.get_next_batch(train_batch)
                    train_batch_data = create_word2vec.indexMat2vector_lookup(w2vModel, train_batch_data)
    
                    train_step(train_batch_data, train_batch_label)
                    current_step = tf.train.global_step(sess, global_step)
    
                    if current_step % val_step == 0:
                        val_losses = []
                        val_accs = []
                        # for k in range(int(val_nums/batch_size)):
                        for k in range(100):
                            val_batch_data, val_batch_label = create_batch_data.get_next_batch(val_batch)
                            val_batch_data = create_word2vec.indexMat2vector_lookup(w2vModel, val_batch_data)
                            val_loss, val_acc=dev_step(val_batch_data, val_batch_label, writer=dev_summary_writer)
                            val_losses.append(val_loss)
                            val_accs.append(val_acc)
                        mean_loss = np.array(val_losses, dtype=np.float32).mean()
                        mean_acc = np.array(val_accs, dtype=np.float32).mean()
                        print("--------Evaluation:step {}, loss {:g}, acc {:g}".format(current_step, mean_loss, mean_acc))
    
                    if current_step % snapshot == 0:
                        path = saver.save(sess, checkpoint_prefix, global_step=current_step)
                        print("Saved model checkpoint to {}\n".format(path))
    
    
    def main():
        # Data preprocess
        labels_file = 'data/THUCNews_labels.txt'
        word2vec_path = "../word2vec/models/THUCNews_word2Vec/THUCNews_word2Vec_128.model"
    
        max_steps = 100000  # 迭代次数
        batch_size = 128
    
        out_dir = "./models"  # 模型ckpt和summaries输出的目录
        train_dir = './data/train_data'
        val_dir = './data/val_data'
    
        train(train_dir=train_dir,
              val_dir=val_dir,
              labels_file=labels_file,
              word2vec_path=word2vec_path,
              batch_size=batch_size,
              max_steps=max_steps,
              log_step=50,
              val_step=500,
              snapshot=1000,
              out_dir=out_dir)
    
    
    if __name__=="__main__":
        main()

--------------------

## 五、测试过程 ##

这里提供两种测试方法：

**（1）：text\_predict(files\_list, labels\_file, models\_path, word2vec\_path, batch\_size)**

该方法，可以直接测试待分类的中文文本

**（2）：batch\_predict(val\_dir,labels\_file,models\_path,word2vec\_path,batch\_size)**

该方法，用于批量测试，val\_dir目录保存的是测试数据的npy文件，这些文件都是上面用word2vec词向量处理THUCNews数据文件。

#! /usr/bin/env python
    # encoding: utf-8
    
    import tensorflow as tf
    import numpy as np
    import os
    from text_cnn import TextCNN
    from utils import create_batch_data, create_word2vec, files_processing
    import math
    
    
    def text_predict(files_list, labels_file, models_path, word2vec_path, batch_size):
        '''
        预测...
        :param val_dir:   val数据目录
        :param labels_file:  labels文件目录
        :param models_path:  模型文件
        :param word2vec_path: 词向量模型文件
        :param batch_size: batch size
        :return:
        '''
        max_sentence_length = 300
        embedding_dim = 128
        filter_sizes = [3, 4, 5, 6]
        num_filters = 200  # Number of filters per filter size
        l2_reg_lambda = 0.0  # "L2 regularization lambda (default: 0.0)
        print("Loading data...")
        w2vModel = create_word2vec.load_wordVectors(word2vec_path)
    
        labels_set = files_processing.read_txt(labels_file)
        labels_nums = len(labels_set)
        sample_num=len(files_list)
    
        labels_list=[-1]
        labels_list=labels_list*sample_num
    
        with tf.Graph().as_default():
            sess = tf.Session()
            with sess.as_default():
                cnn = TextCNN(sequence_length = max_sentence_length,
                              num_classes = labels_nums,
                              embedding_size = embedding_dim,
                              filter_sizes = filter_sizes,
                              num_filters = num_filters,
                              l2_reg_lambda = l2_reg_lambda)
    
                # Initialize all variables
                sess.run(tf.global_variables_initializer())
                saver = tf.train.Saver()
                saver.restore(sess, models_path)
    
                def pred_step(x_batch):
                    """
                    predictions model on a dev set
                    """
                    feed_dict = {
                      cnn.input_x: x_batch,
                      cnn.dropout_keep_prob: 1.0
                    }
                    pred = sess.run([cnn.predictions],feed_dict)
                    return pred
    
                batchNum = int(math.ceil(1.0 * sample_num / batch_size))
                for i in range(batchNum):
                    start = i * batch_size
                    end = min((i + 1) * batch_size, sample_num)
                    batch_files = files_list[start:end]
    
                    # 读取文件内容，字词分割
                    batch_content= files_processing.read_files_list_to_segment(batch_files,
                                                                    max_sentence_length,
                                                                    padding_token='<PAD>')
                    # [1]将字词转为索引矩阵,再映射为词向量
                    batch_indexMat = create_word2vec.word2indexMat(w2vModel, batch_content, max_sentence_length)
                    val_batch_data = create_word2vec.indexMat2vector_lookup(w2vModel, batch_indexMat)
    
                    # [2]直接将字词映射为词向量
                    # val_batch_data = create_word2vec.word2vector_lookup(w2vModel,batch_content)
    
                    pred=pred_step(val_batch_data)
                     
                    pred=pred[0].tolist()
                    pred=files_processing.labels_decoding(pred,labels_set)
                    for k,file in enumerate(batch_files):
                        print("{}, pred:{}".format(file,pred[k]))
    
    def batch_predict(val_dir,labels_file,models_path,word2vec_path,batch_size):
        '''
        预测...
        :param val_dir:   val数据目录
        :param labels_file:  labels文件目录
        :param models_path:  模型文件
        :param word2vec_path: 词向量模型文件
        :param batch_size: batch size
        :return:
        '''
        max_sentence_length = 300
        embedding_dim = 128
        filter_sizes = [3, 4, 5, 6]
        num_filters = 200  # Number of filters per filter size
        l2_reg_lambda = 0.0  # "L2 regularization lambda (default: 0.0)
        print("Loading data...")
        w2vModel = create_word2vec.load_wordVectors(word2vec_path)
    
        labels_set = files_processing.read_txt(labels_file)
        labels_nums = len(labels_set)
    
    
        val_file_list = create_batch_data.get_file_list(file_dir=val_dir, postfix='*.npy')
        val_batch = create_batch_data.get_data_batch(val_file_list, labels_nums=labels_nums, batch_size=batch_size,
                                                     shuffle=False, one_hot=True)
    
        print("val data   info *****************************")
        val_nums = create_word2vec.info_npy(val_file_list)
        print("labels_set info *****************************")
        files_processing.info_labels_set(labels_set)
        # Training
        with tf.Graph().as_default():
            sess = tf.Session()
            with sess.as_default():
                cnn = TextCNN(sequence_length = max_sentence_length,
                              num_classes = labels_nums,
                              embedding_size = embedding_dim,
                              filter_sizes = filter_sizes,
                              num_filters = num_filters,
                              l2_reg_lambda = l2_reg_lambda)
    
                # Initialize all variables
                sess.run(tf.global_variables_initializer())
                saver = tf.train.Saver()
                saver.restore(sess, models_path)
    
                def dev_step(x_batch, y_batch):
                    """
                    Evaluates model on a dev set
                    """
                    feed_dict = {
                      cnn.input_x: x_batch,
                      cnn.input_y: y_batch,
                      cnn.dropout_keep_prob: 1.0
                    }
                    loss, accuracy = sess.run(
                        [cnn.loss, cnn.accuracy],
                        feed_dict)
                    return loss, accuracy
    
                val_losses = []
                val_accs = []
                for k in range(int(val_nums/batch_size)):
                # for k in range(int(10)):
                    val_batch_data, val_batch_label = create_batch_data.get_next_batch(val_batch)
                    val_batch_data = create_word2vec.indexMat2vector_lookup(w2vModel, val_batch_data)
                    val_loss, val_acc=dev_step(val_batch_data, val_batch_label)
                    val_losses.append(val_loss)
                    val_accs.append(val_acc)
                    print("--------Evaluation:step {}, loss {:g}, acc {:g}".format(k, val_loss, val_acc))
    
                mean_loss = np.array(val_losses, dtype=np.float32).mean()
                mean_acc = np.array(val_accs, dtype=np.float32).mean()
                print("--------Evaluation:step {}, mean loss {:g}, mean acc {:g}".format(k, mean_loss, mean_acc))
    
    
    def main():
        # Data preprocess
        labels_file = 'data/THUCNews_labels.txt'
        # word2vec_path = 'word2vec/THUCNews_word2vec300.model'
        word2vec_path = "../word2vec/models/THUCNews_word2Vec/THUCNews_word2Vec_128.model"
        models_path='models/checkpoints/model-30000'
        batch_size = 128
        val_dir = './data/val_data'
    
        batch_predict(val_dir=val_dir,
              labels_file=labels_file,
              models_path=models_path,
              word2vec_path=word2vec_path,
              batch_size=batch_size)
    
        test_path='/home/ubuntu/project/tfTest/THUCNews/my_test'
        files_list = files_processing.get_files_list(test_path,postfix='*.txt')
        text_predict(files_list, labels_file, models_path, word2vec_path, batch_size)
    
    if __name__=="__main__":
        main()

[nlp-learning-tutorials_THUCNews at master _ PanJinquan_nlp-learning-tutorials _ GitHub]: https://github.com/PanJinquan/nlp-learning-tutorials/tree/master/THUCNews
[THUCTC_]: http://thuctc.thunlp.org/message
[Link 1]: https://pan.baidu.com/s/1DT5xY9m2yfu1YGaGxpWiBQ
[Link 2]: https://pan.baidu.com/s/1n4ZgiF0gbY0zsK0706wZiw
[Link 3]: https://pan.baidu.com/s/12Hdf36QafQ3y6KgV_vLTsw
[20181114190800584.png]: /images/20220417/1bdc2c97f7dd4b0f961e941b38ec2b8f.png
[THUCTC_ 1]: http://thuctc.thunlp.org/
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2d1eXVlYWxpYW4_size_16_color_FFFFFF_t_70]: /images/20220417/3d17de8278fe49619da4e0d5b1720fd9.png
[gensim_word2vec]: https://blog.csdn.net/guyuealian/article/details/84072158